Volume 14 | Issue 4 |

 Abstract

Modern digital platforms use AI to show users personalized ads and recommendations based on their past behavior. However, most users do not clearly understand why a particular product or advertisement is shown to them. This lack of transparency often creates confusion and reduces trust in such systems. In this paper, we present T-Trace, a simple and transparent framework that helps track how a user's past actions influence their current decisions. Instead of only explaining why an ad appears, the system tries to connect a user's final decision (such as making a purchase) with earlier activities like searching, browsing, or comparing products. The proposed system collects user interaction data and processes it using a lightweight machine learning model. It then uses SHAP-based explanations to highlight which past actions had the most impact on a particular decision. To maintain reliability, each decision and its explanation are stored in a hash-linked structure, making it difficult to alter past records. We also developed a Streamlit-based dashboard that presents these insights in an easy-to-understand format using timelines and simple explanations. Through this approach, the system aims to improve transparency, help users better understand their own decisions, and support ethical use of AI in personalization systems.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Explainable AI, Behavioral Influence, User Decision Tracking, SHAP, Transparency, AI Ethics, Recommender Systems, Digital Marketing, ML Explainability, Streamlit

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Modern software deployment can be challenging, especially for beginners, as tools like Docker still require manual Dockerfile creation, which is complex and error-prone. This research introduces a Smart Deployment Assistant that uses Generative AI to automatically generate Dockerfiles from inputs such as GitHub repositories, ZIP files, or images. Built with Streamlit and Python, the system analyzes project data and produces optimized configurations, significantly reducing time and improving accuracy compared to manual methods. Overall, it simplifies deployment and makes it more accessible, efficient, and user-friendly for developers.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Generative AI, Docker, Dockerfile Automation, DevOps, Software Deployment, Streamlit, Containerization, Artificial Intelligence, Automation Tools, Cloud Deployment

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The Fatal Accidents Act, 1855 is a law that allows the family of a person who died due to someone else's negligence or wrongful act to claim compensation. We use it to ensure that dependents like spouse, children, or parents get financial support and justice after such a loss.

Licence: creative commons attribution 4.0

  License

Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Wrongful act, negligence

  License

Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Cloud computing has transformed the way data is stored and accessed, offering scalability and convenience, but it has also brought forward notable security concerns. This study examines the role of ethical hacking in detecting vulnerabilities within cloud environments and reducing associated risks. It emphasizes techniques such as penetration testing, the use of vulnerability assessment tools, and effective risk mitigation strategies. The findings indicate that a proactive approach through ethical hacking plays a crucial role in strengthening cloud security by identifying and addressing threats before they can be exploited.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Cloud Security, Ethical Hacking, Vulnerability Assessment, Penetration Testing, Risk Mitigation, Cyber Security

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Rapid digitization across industries has made cybersecurity a paramount concern for organizations worldwide. Modern computing environments generate enormous volumes of data including authentication logs, network flows, application events, and endpoint telemetry -- volumes that far exceed human capacity for manual inspection. Artificial Intelligence (AI) has emerged as a transformative force in cyber defense, enabling systems to learn behavioral patterns, identify anomalies, classify threats, and facilitate faster incident response. This paper presents a comprehensive examination of AI-driven approaches for real-time threat detection and prevention. Core topics include intrusion detection, malware analysis, phishing identification, user behavior analytics, and automated security operations. Classical approaches such as Random Forest and Support Vector Machine are examined alongside deep learning architectures including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformer models. The paper additionally addresses persistent challenges including data quality issues, adversarial robustness, model drift, explainability demands, and infrastructure constraints. Findings indicate that while AI substantially enhances detection capabilities, its effective deployment depends on robust data pipelines, continuous monitoring, and collaborative human-AI workflows.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence, Cybersecurity, Threat Detection, Intrusion Detection Systems, Malware Classification, Phishing Detection, Deep Learning, Anomaly Detection, Security Analytics, Behavior Analytics

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Large language models have moved from research curiosities to systems used by millions, yet questions about whether they can be trusted remain largely unsettled. This review brings together recent work on three closely linked problems: trustworthiness, hallucination, and evaluation. We surveyed fifteen studies published between 2020 and 2025 covering safety alignment, factual reliability, retrieval-augmented generation, benchmark design, and human evaluation methods. The picture that emerges is one of rapid progress paired with stubborn limitations. Hallucination remains common even in frontier models, partly because it is built into how these systems are trained. Evaluation methods often disagree with each other, and benchmarks tend to age quickly as models adapt to them. Trust frameworks are getting richer, but most still treat issues like bias, toxicity, and factual accuracy as separate problems rather than parts of a whole. We argue that future work needs to combine technical metrics with user-centered studies, and that retrieval grounding, while useful, is not a complete fix. The paper closes with a discussion of open challenges and what we think are the more promising research directions.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Large Language Models, Trustworthiness, Hallucination, Evaluation, Retrieval-Augmented Generation, AI Safety, Benchmarks.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Crime entertainment media (CEM) has emerged as one of the most pervasive and psychologically influential content genres consumed by contemporary adolescents across television, streaming platforms, and social media. This article provides a conceptual and psychological review of the empirical and theoretical literature examining how exposure to crime-themed entertainment shapes adolescent cognition, affect, behavior, and identity development. Drawing on neurobiological, social learning, developmental, and sociological frameworks--including the work of Bandura, Erikson, Merton, and Gerbner--the article synthesizes evidence that CEM activates reward and threat-detection circuitry, functions as an identity scaffold during psychosocial development, and may amplify pre-existing vulnerabilities in susceptible youth. The review finds that while CEM does not function as a direct cause of criminal behavior, its effects on aggressive cognition, emotional dysregulation, fear perception, institutional distrust, and social desensitization are empirically documented, particularly in the context of chronic, unsupervised, or high-intensity consumption. Critically, the article advances a differential susceptibility framework, arguing that media-driven behavioral outcomes are mediated by biological reactivity, psychological trait profiles, parental monitoring, and media literacy. Implications for mental health practitioners, educators, and media policymakers are discussed, alongside recommendations for future empirical research.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

crime entertainment media, adolescent development, media violence, social learning theory, differential susceptibility, neurobiological arousal, identity formation, aggression

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study examines the role of advertising in shaping and reinforcing beauty and fitness ideals in contemporary society, with a particular focus on digital media and influencer culture. Drawing on sociological perspectives such as social comparison theory and media representation, the paper analyses how advertisements construct gendered body ideals and normalize narrow standards of attractiveness. The study highlights that women are often portrayed as slim, fair, and flawless, while men are depicted as muscular and dominant, reinforcing stereotypical notions of femininity and masculinity. With the rise of platforms such as Instagram and YouTube, advertising has become more pervasive and personalized, increasing its impact on audience perceptions. Influencer marketing further intensifies this process by presenting curated and idealized lifestyles as attainable realities. The findings suggest that such representations contribute to body dissatisfaction, low self-esteem, and the commodification of the human body. The study concludes by emphasizing the need for inclusive media practices and greater critical awareness among audiences to challenge unrealistic beauty standards and promote healthier, more diverse representations.

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Beauty Standards; Fitness Culture; Advertisements; Media Representation; Body Image; Consumer Culture.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

FÜHRUNG AUS DER MITTE: SOFT POWER UND ZÖGERLICHE HEGEMONIE IN DER EU- KRISENDIPLOMATIE DEUTSCHLANDS UNTER ANGELA MERKEL (2005-2021)

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

FUHRUNG AUS DER MITTE: SOFT POWER UND ZÖGERLICHE HEGEMONIE IN DER EU- KRISENDIPLOMATIE DEUTSCHLANDS UNTER ANGELA MERKEL (2005-2021)

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Licence: creative commons attribution 4.0

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Biopower, Indian Mythology, Caste, Gender, Sovereignty, Counter-narratives

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Creative Commons Attribution 4.0 and The Open Definition